deliberative-calibration

Research

Strategy for small-N complete pairwise comparison using Bradley-Terry, Thurstone, AHP, and Borda methods to produce calibrated rankings.

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Deliberative Calibration

Purpose

Produce a fully calibrated ranking when the candidate set is small enough (5-15 items) to allow complete or near-complete pairwise comparison. Leverages parametric models (Bradley-Terry, Thurstone) and structured weighting (AHP) to extract maximum information from each comparison.

When to use

Candidate count N ≤ 15
Complete comparison matrix is feasible (N(N-1)/2 pairs manageable)
High precision required — every rank position matters
Calibrated strength scores needed, not just ordinal ranking

Budget

Resource	Allocation
Comparisons	N(N-1)/2 (complete) or ≥ N×log(N) (near-complete)
Iterations	2-4 rounds (initial + consistency repair)
Convergence target	CR < 0.1, rating stability ≥ 95%

State Ledger

candidates: []          # list of items being ranked
comparison_matrix: {}   # pair → {winner, confidence, reasoning}
ratings: {}             # candidate → score
method: ""              # bradley-terry | thurstone | ahp | borda
iteration: 0
convergence: {stable: false, score: 0.0}
consistency: {cr: null, cycles: []}

Available Tactics

adaptive-pair-selection — select next pairs by information gain, compare, update, check convergence
consistency-audit-loop — verify transitivity, repair inconsistencies

Available SOPs

pair-selector
comparison-executor
rating-update
convergence-check
cycle-detection
inconsistency-localization
ranking-synthesis

Execution Guidance

Initialize ratings uniformly for all candidates
Run adaptive-pair-selection tactic until convergence or complete matrix
Run consistency-audit-loop to verify transitivity
If CR > 0.1, re-compare flagged pairs and recompute
Produce final ranking via ranking-synthesis

Output Format

ranking:
  - {rank: 1, candidate: "...", score: 0.95, ci: [0.91, 0.99]}
  - {rank: 2, candidate: "...", score: 0.82, ci: [0.77, 0.87]}
method: bradley-terry
consistency_ratio: 0.04
total_comparisons: 28
convergence_iterations: 3

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
adaptive-pair-selection	Iteratively select maximally informative pairs, execute comparisons, update ratings, and check convergence until ranking stabilizes.
consistency-audit-loop	Detect preference cycles, localize inconsistent judgments, request corrections, and recompute ratings until consistency threshold is met.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
ranking-synthesis	Produce the final ranking artifact from converged ratings and consistency report.